Yangning Li
Papers
1
Total Citations
42
H-Index
1
About
Yangning Li has made significant contributions to distributed artificial intelligence and multi-robot systems, with a primary focus on cooperative control and reinforcement learning. Their most-cited work, "Distributed multi‐agent deep reinforcement learning for cooperative multi‐robot pursuit" (2020, 42 citations), addresses the classic multi-robot pursuit problem—a key benchmark for evaluating coordination strategies in multi-agent environments. By leveraging deep reinforcement learning, Li developed a distributed framework that enables robots to autonomously learn cooperative behaviors for tasks such as target interception, significantly advancing the practical deployment of intelligent multi-robot teams. This research not only demonstrates robust performance in dynamic scenarios but also provides a scalable solution for real-world applications like search-and-rescue and surveillance. Li’s work bridges the gap between theoretical multi-agent learning and applied robotics, earning recognition for its clarity and impact. With growing citations reflecting its influence, Li continues to shape the field of distributed AI, inspiring further exploration into adaptive, decentralized decision-making systems.
Research Focus
Key Achievements
Top Papers
- 1